Analysis of complex neural circuits with nonlinear multidimensional hidden state models
Name
Friedman-2016-Analysis of complex.pdf
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919.48 KB
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Checksum (MD5)
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Author(s)
Altshuler, Alex
Sholes, Jacquelyn E. C.
Friedman, Alexander
Slocum, Joshua Foster
Tyulmankov, Danil
Gibb, Leif G.
Ruangwises, Suthee
Shi, Qinru
Toro Arana, Sebastian
Beck, Dirk W.
Date Issued
June 2016
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Friedman, Alexander et al. “Analysis of Complex Neural Circuits with Nonlinear Multidimensional Hidden State Models.” Proceedings of the National Academy of Sciences 113.23 (2016): 6538–6543. © 2016 National Academy of Sciences
Version
Final published version
Abstract
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1606280113
